The penalized Cox proportional hazard model is a popular analytical approach for survival data with a large number of covariates. Such problems are especially challenging when covariates vary over follow-up time (i.e., the covariates are time-dependent). The standard R packages for fully penalized Cox models cannot currently incorporate time-dependent covariates. To address this gap, we implement a variant of gradient descent algorithm (proximal gradient descent) for fitting penalized Cox models. We apply our implementation to real and simulated data sets.
翻译:受罚的考克斯比例危害模型是针对大量共变体的存活数据的一种流行分析方法,当随后续时间的不同而出现共变时(即共变体取决于时间),这些问题尤其具有挑战性。完全受罚的考克斯模型的标准R包目前不能包含时间上的共变体。为弥补这一差距,我们采用了梯度下降算法(精度梯度梯度下降)变量,以适应受罚的考克斯模型。我们把我们的实施情况应用到真实和模拟的数据集中。